Overview

Dataset statistics

Number of variables6
Number of observations1000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory47.0 KiB
Average record size in memory48.1 B

Variable types

NUM6

Reproduction

Analysis started2020-08-25 00:32:27.940788
Analysis finished2020-08-25 00:32:34.378748
Duration6.44 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

oz1 has unique values Unique
oz2 has unique values Unique
oz3 has unique values Unique
oz4 has unique values Unique
oz5 has unique values Unique
target has unique values Unique

Variables

oz1
Real number (ℝ)

UNIQUE

Distinct count1000
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-6.505870242534684e-10
Minimum-2.3212990760803223
Maximum2.285586357116699
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T00:32:34.428692image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-2.321299076
5-th percentile-1.638232827
Q1-0.7304718643
median-0.01378638251
Q30.7746094167
95-th percentile1.68369987
Maximum2.285586357
Range4.606885433
Interquartile range (IQR)1.505081281

Descriptive statistics

Standard deviation1.000000001
Coefficient of variation (CV)-1537073387
Kurtosis-0.7140246763
Mean-6.505870243e-10
Median Absolute Deviation (MAD)0.7584579289
Skewness-0.01682955871
Sum-6.505870243e-07
Variance1.000000002
2020-08-25T00:32:34.531639image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1.36718642710.1%
 
0.141271904110.1%
 
0.315757811110.1%
 
0.388999879410.1%
 
-1.95834112210.1%
 
-1.06380486510.1%
 
-0.896288216110.1%
 
-0.28334122910.1%
 
-0.0843095555910.1%
 
-0.0262042954610.1%
 
-1.37238514410.1%
 
0.842436790510.1%
 
-0.692068338410.1%
 
-0.245764434310.1%
 
0.686182141310.1%
 
-0.259684294510.1%
 
-2.07407450710.1%
 
1.12626564510.1%
 
-0.466136485310.1%
 
-0.516254782710.1%
 
0.78578478110.1%
 
-1.58328795410.1%
 
-0.588517606310.1%
 
-0.466742873210.1%
 
-0.943979620910.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-2.32129907610.1%
 
-2.24265909210.1%
 
-2.12853193310.1%
 
-2.11168026910.1%
 
-2.07928729110.1%
 
-2.0775942810.1%
 
-2.07639360410.1%
 
-2.07407450710.1%
 
-2.07195925710.1%
 
-2.06182980510.1%
 
ValueCountFrequency (%) 
2.28558635710.1%
 
2.28372049310.1%
 
2.23836827310.1%
 
2.20749211310.1%
 
2.13935279810.1%
 
2.13170456910.1%
 
2.1285233510.1%
 
2.09004306810.1%
 
2.08212947810.1%
 
2.04351115210.1%
 

oz2
Real number (ℝ)

UNIQUE

Distinct count1000
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.8167193061024988e-09
Minimum-1.7634522914886477
Maximum1.7378325462341309
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T00:32:34.647754image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.763452291
5-th percentile-1.587667054
Q1-0.813601777
median-0.0161343934
Q30.8742136806
95-th percentile1.523936546
Maximum1.737832546
Range3.501284838
Interquartile range (IQR)1.687815458

Descriptive statistics

Standard deviation1.000000001
Coefficient of variation (CV)-550442766.8
Kurtosis-1.158879381
Mean-1.816719306e-09
Median Absolute Deviation (MAD)0.8562484682
Skewness-0.02344674251
Sum-1.816719306e-06
Variance1.000000003
2020-08-25T00:32:34.754320image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1.52344226810.1%
 
0.918614983610.1%
 
-1.00915503510.1%
 
0.232588797810.1%
 
-1.14195895210.1%
 
-1.53773105110.1%
 
-0.977228045510.1%
 
-0.684257507310.1%
 
1.00132644210.1%
 
0.854605436310.1%
 
1.16928970810.1%
 
-0.537768542810.1%
 
-1.48959684410.1%
 
0.752606093910.1%
 
0.805338501910.1%
 
-0.157387688810.1%
 
1.61059784910.1%
 
0.267899900710.1%
 
-0.835151851210.1%
 
1.55205440510.1%
 
1.54415214110.1%
 
0.177404493110.1%
 
-0.216955125310.1%
 
0.27180036910.1%
 
1.38580155410.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-1.76345229110.1%
 
-1.76143324410.1%
 
-1.75787043610.1%
 
-1.7563198810.1%
 
-1.75018048310.1%
 
-1.74445557610.1%
 
-1.74264204510.1%
 
-1.73719382310.1%
 
-1.73701143310.1%
 
-1.73674881510.1%
 
ValueCountFrequency (%) 
1.73783254610.1%
 
1.73779642610.1%
 
1.73773634410.1%
 
1.73712742310.1%
 
1.73319745110.1%
 
1.73124134510.1%
 
1.7229788310.1%
 
1.71564543210.1%
 
1.70749986210.1%
 
1.70206475310.1%
 

oz3
Real number (ℝ)

UNIQUE

Distinct count1000
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0696094250306487e-09
Minimum-1.7151861190795898
Maximum1.7082360982894895
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T00:32:34.875355image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.715186119
5-th percentile-1.548216617
Q1-0.8726149946
median-0.02860492654
Q30.8727170974
95-th percentile1.558386046
Maximum1.708236098
Range3.423422217
Interquartile range (IQR)1.745332092

Descriptive statistics

Standard deviation0.9999999996
Coefficient of variation (CV)934920706.7
Kurtosis-1.188324895
Mean1.069609425e-09
Median Absolute Deviation (MAD)0.8716680128
Skewness0.004809608664
Sum1.069609425e-06
Variance0.9999999992
2020-08-25T00:32:34.980423image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-1.62109124710.1%
 
-0.962223768210.1%
 
1.00918960610.1%
 
1.56778168710.1%
 
-1.24651539310.1%
 
-1.48577654410.1%
 
1.04852390310.1%
 
-0.480809599210.1%
 
-0.160814732310.1%
 
1.13060092910.1%
 
1.07270932210.1%
 
-0.00763465650410.1%
 
1.27652788210.1%
 
-1.14196479310.1%
 
0.281699508410.1%
 
-1.54820692510.1%
 
0.0860159620610.1%
 
0.587829828310.1%
 
1.18101501510.1%
 
-0.961599528810.1%
 
-0.55534678710.1%
 
1.40493214110.1%
 
0.697635054610.1%
 
-0.594400823110.1%
 
-1.23567295110.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-1.71518611910.1%
 
-1.71386444610.1%
 
-1.70832610110.1%
 
-1.70532751110.1%
 
-1.70475530610.1%
 
-1.70265388510.1%
 
-1.70032286610.1%
 
-1.69807374510.1%
 
-1.69599974210.1%
 
-1.69501817210.1%
 
ValueCountFrequency (%) 
1.70823609810.1%
 
1.7080850610.1%
 
1.7079176910.1%
 
1.70625984710.1%
 
1.70582652110.1%
 
1.70530700710.1%
 
1.70355594210.1%
 
1.70191383410.1%
 
1.69889271310.1%
 
1.69830763310.1%
 

oz4
Real number (ℝ)

UNIQUE

Distinct count1000
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.5180557966232299e-09
Minimum-1.6968183517456057
Maximum1.7647590637207031
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T00:32:35.096877image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.696818352
5-th percentile-1.52595343
Q1-0.8629742414
median-0.02831385378
Q30.8674184829
95-th percentile1.558097589
Maximum1.764759064
Range3.461577415
Interquartile range (IQR)1.730392724

Descriptive statistics

Standard deviation1.000000002
Coefficient of variation (CV)-658737316.8
Kurtosis-1.202317479
Mean-1.518055797e-09
Median Absolute Deviation (MAD)0.8667578781
Skewness0.05211384297
Sum-1.518055797e-06
Variance1.000000005
2020-08-25T00:32:35.201346image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-1.20702993910.1%
 
1.5091874610.1%
 
1.06000781110.1%
 
0.685977876210.1%
 
0.126776874110.1%
 
-1.54047095810.1%
 
-0.393433421910.1%
 
1.47015523910.1%
 
-0.310159176610.1%
 
-0.5944480310.1%
 
0.522175788910.1%
 
1.74706542510.1%
 
-0.32358735810.1%
 
-1.6392470610.1%
 
1.26309466410.1%
 
-0.460177689810.1%
 
-0.944669067910.1%
 
-1.44497132310.1%
 
0.315769672410.1%
 
0.746773302610.1%
 
-1.01526904110.1%
 
-1.1146366610.1%
 
-0.0133019117610.1%
 
-1.14978861810.1%
 
-0.118568219210.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-1.69681835210.1%
 
-1.69124805910.1%
 
-1.69090402110.1%
 
-1.68806207210.1%
 
-1.68800282510.1%
 
-1.68666613110.1%
 
-1.68150889910.1%
 
-1.66729152210.1%
 
-1.6665636310.1%
 
-1.6618748910.1%
 
ValueCountFrequency (%) 
1.76475906410.1%
 
1.76406061610.1%
 
1.76102197210.1%
 
1.75664317610.1%
 
1.75635170910.1%
 
1.75546884510.1%
 
1.74848127410.1%
 
1.74753093710.1%
 
1.74706542510.1%
 
1.74361765410.1%
 

oz5
Real number (ℝ)

UNIQUE

Distinct count1000
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.538765203207731e-10
Minimum-1.7048542499542236
Maximum1.7417409420013428
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T00:32:35.320142image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.70485425
5-th percentile-1.54484759
Q1-0.8762024939
median0.02282036189
Q30.8389086872
95-th percentile1.54060204
Maximum1.741740942
Range3.446595192
Interquartile range (IQR)1.715111181

Descriptive statistics

Standard deviation0.9999999988
Coefficient of variation (CV)1326477177
Kurtosis-1.199974369
Mean7.538765203e-10
Median Absolute Deviation (MAD)0.863940605
Skewness-0.006917016778
Sum7.538765203e-07
Variance0.9999999976
2020-08-25T00:32:35.424194image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.19921934610.1%
 
-0.520149052110.1%
 
1.31378805610.1%
 
-1.00663685810.1%
 
0.50052571310.1%
 
0.772120177710.1%
 
-0.348949998610.1%
 
-0.355050474410.1%
 
0.600240945810.1%
 
0.353830367310.1%
 
-1.05933463610.1%
 
0.893205344710.1%
 
1.51597595210.1%
 
-0.725230693810.1%
 
-1.65748834610.1%
 
-0.256765544410.1%
 
-0.839413285310.1%
 
1.15747785610.1%
 
-1.47323250810.1%
 
-0.388977736210.1%
 
-0.842827200910.1%
 
-0.563108563410.1%
 
-1.2004247910.1%
 
1.13259935410.1%
 
-1.44801998110.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-1.7048542510.1%
 
-1.69770729510.1%
 
-1.69700992110.1%
 
-1.69416761410.1%
 
-1.68025064510.1%
 
-1.67916774710.1%
 
-1.67842054410.1%
 
-1.67709541310.1%
 
-1.67547428610.1%
 
-1.67331385610.1%
 
ValueCountFrequency (%) 
1.74174094210.1%
 
1.73572301910.1%
 
1.72929060510.1%
 
1.72902810610.1%
 
1.72530674910.1%
 
1.72184574610.1%
 
1.72032356310.1%
 
1.7176946410.1%
 
1.71477639710.1%
 
1.71056008310.1%
 

target
Real number (ℝ)

UNIQUE

Distinct count1000
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-9.798677638173103e-10
Minimum-2.727972507476806
Maximum2.5458414554595947
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T00:32:35.539898image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-2.727972507
5-th percentile-1.773523962
Q1-0.6876930147
median0.1305322945
Q30.7683034539
95-th percentile1.464644134
Maximum2.545841455
Range5.273813963
Interquartile range (IQR)1.455996469

Descriptive statistics

Standard deviation1.000000002
Coefficient of variation (CV)-1020545872
Kurtosis-0.4971102129
Mean-9.798677638e-10
Median Absolute Deviation (MAD)0.6915079951
Skewness-0.3644749471
Sum-9.798677638e-07
Variance1.000000004
2020-08-25T00:32:35.641903image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-0.188963666610.1%
 
1.30594444310.1%
 
0.443863004410.1%
 
-1.72786712610.1%
 
1.39015376610.1%
 
0.623694121810.1%
 
-2.7057075510.1%
 
-0.915370047110.1%
 
0.426103472710.1%
 
1.09603261910.1%
 
0.494149059110.1%
 
-0.578761637210.1%
 
-1.62763869810.1%
 
0.102129876610.1%
 
-1.94324195410.1%
 
-1.4290798910.1%
 
0.207693263910.1%
 
-2.0103166110.1%
 
-0.162264972910.1%
 
-1.17702436410.1%
 
-1.25905406510.1%
 
-0.0535054542110.1%
 
-0.0443498827510.1%
 
0.322937637610.1%
 
1.02114093310.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-2.72797250710.1%
 
-2.72100043310.1%
 
-2.7057075510.1%
 
-2.55006170310.1%
 
-2.5276408210.1%
 
-2.49050641110.1%
 
-2.45821785910.1%
 
-2.40398502310.1%
 
-2.36932492310.1%
 
-2.29481339510.1%
 
ValueCountFrequency (%) 
2.54584145510.1%
 
2.16048908210.1%
 
2.15800261510.1%
 
2.05945706410.1%
 
2.03319144210.1%
 
1.97962880110.1%
 
1.96888828310.1%
 
1.91110789810.1%
 
1.86850035210.1%
 
1.8605810410.1%
 

Interactions

2020-08-25T00:32:28.192815image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:32:28.335428image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:32:28.486786image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:32:28.638513image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:32:28.791472image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:32:28.955403image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:32:29.101467image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:32:29.257119image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:32:29.417713image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:32:29.579582image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:32:29.745994image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:32:29.908967image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:32:30.063316image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:32:30.218228image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:32:30.380046image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:32:30.540658image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:32:30.703026image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:32:30.864471image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:32:31.018695image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:32:31.177763image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:32:31.340636image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:32:31.502078image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:32:31.664325image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:32:31.832301image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:32:32.152858image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:32:32.313215image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:32:32.475125image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:32:32.637352image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:32:32.803993image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:32:32.966192image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:32:33.121267image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:32:33.265839image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:32:33.415708image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:32:33.567159image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:32:33.723640image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:32:33.877914image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-08-25T00:32:35.913843image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-08-25T00:32:36.089403image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-08-25T00:32:36.264636image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-08-25T00:32:36.448866image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-08-25T00:32:34.112229image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:32:34.304145image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

oz1oz2oz3oz4oz5target
00.8276130.874057-0.2696121.0846590.807070-1.051214
1-1.050347-0.809026-1.1895560.9539110.7787971.285375
2-0.0896530.129047-0.360108-1.1081250.066154-0.112975
3-0.804896-0.6461720.037350-1.6909040.4187160.037544
4-0.261358-0.5830270.242059-1.4556861.5975450.476958
5-0.639735-0.186486-1.2855170.322408-0.7175831.080172
61.2048641.0971911.1784500.951471-0.334418-0.722852
7-1.324063-1.405459-0.4506310.5717640.9370510.544446
8-0.933337-1.4228991.180583-1.184382-1.412030-0.302630
9-0.516255-0.4037670.333873-0.0250901.5105941.111125

Last rows

oz1oz2oz3oz4oz5target
990-0.965969-0.578423-1.375615-0.346220-1.2704990.587205
991-0.0119431.011965-1.687013-1.382596-1.469494-1.628310
9920.3298781.551032-1.303457-1.0966860.556655-1.721149
9930.386392-0.3328240.961910-0.8298940.2997540.230917
9941.4609331.3285610.819097-0.3882341.0635310.203408
9951.2334670.8319820.8355680.575001-1.679168-1.534912
996-0.552215-0.196225-1.070973-1.1710211.6368460.809665
9970.6676841.317299-0.940285-0.1864000.847608-1.410159
998-0.407723-0.6476540.4129770.6086231.2954261.313306
999-1.041420-1.0236300.2817001.1881740.5551330.883937